Model averaging in calibration of near-infrared instruments with correlated high-dimensional data.

ABSTRACT: Model averaging (MA) is a modelling strategy where the uncertainty in the configuration of selected variables is taken into account by weight-combining each estimate of the so-called 'candidate model'. Some studies have shown that MA enables better prediction, even in high-dimensional cases. However, little is known about the model prediction performance at different types of multicollinearity in high-dimensional data. Motivated by calibration of near-infrared (NIR) instruments,we focus on MA prediction performance in such data. The weighting schemes that we consider are based on the Akaike's information criterion (AIC), Mallows' C_p, and cross-validation. For estimating the model parameters, we consider the standard least squares and the ridge regression methods. The resu

SUBMITTER: Salaki DT 

PROVIDER: S-EPMC10810656 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature